Recent AS2 models are based on Deep Neural Networks (DNNs), which learn distributed representations of the input data and are trained to apply a series of non-linear transformations to the input question and answer, represented as compositions of word or character embeddings. DNN architectures learn answer sentence-relevant patterns using intra-pair similarities as well as cross-pair, question-to-question and answer-to-answer similarities, when modeling the input texts. For example, the CNN network by [1]}
has two separate embedding layers for the question and answer, which are followed by the respective convolution layers. The output of the latter is concatenated and then passed through the final fully-connected joint layer. They also added embeddings encoding relational links between matching words [2]}: a sort of hardcoded attention, which highly increases accuracy.
